Table of Contents
Using data available up to the: 2020-04-03
Expected daily cases by region
Figure 1: The results of the latest reproduction number estimates (based on estimated cases with a date of infection on the 2020-03-24) in Italy, stratified by region, can be summarised by whether cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 cases reported on a single day are not included in the analysis (light grey).
National summary
Summary (estimates as of the 2020-03-24)
Table 1: Latest estimates (as of the 2020-03-24) of the number of cases by date of infection, the expected change in daily cases, the effective reproduction number, the doubling time, and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.| Estimate | |
|---|---|
| New cases by infection date | 5340 (3343 – 7666) |
| Expected change in daily cases | Unsure |
| Effective reproduction no. | 1 (0.8 – 1.3) |
| Doubling time (days) | -100 (14 – Inf) |
| Adjusted R-squared | 0.41 (-0.17 – 0.85) |
Reported cases, their estimated date of infection, and time-varying reproduction number estimates
Figure 2: A.) Cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates are shown until the 2020-03-24.Dark grey ribbon = 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.
Time-varying rate of spread and doubling time
Figure 3: A.) Time-varying estimate of the rate of spread, B.) Time-varying estimate of the doubling time in days (note that when the rate of spread is negative the doubling time is assumed to be infinite), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates are shown until the 2020-03-24. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.
Regional Breakdown
Data availability
- Case counts by date, stratified by region, were constructed from daily datasets made publically available by the Dipartimento della Protezione Civile (Dipartimento della Protezione Civile, n.d.; Abbott et al. 2020).
- Case onset dates were estimated using case counts by date of report and a distribution of reporting delays fitted to an international line-list (Xu et al., n.d.; Abbott et al. 2020).
Limitations
- Line-list data to inform a Italy specific estimate of the reporting delay was not available. This means that we could also not account for any regional differences.
- Regions are only included if at least 40 cases have been reported on a single day.
Summary of latest reproduction number and case count estimates by date of infection
Figure 4: Cases with date of infection on the 2020-03-24 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily cases and shaded based on the expected change in daily cases. The dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.
Reproduction numbers over time in the six regions expected to have the most incident cases
Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-24. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.
Reported cases and their estimated date of infection in the six regions expected to have the most incident cases
Figure 6: Cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-24. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.
Reproduction numbers over time in all regions
Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-24. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.
Reported cases and their estimated date of infection in all regions
Figure 8: Cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-24. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.
Latest estimates (as of the 2020-03-24)
Table 2: Latest estimates (as of the 2020-03-24) of the number of cases by date of infection, the effective reproduction number, and the doubling time in each region. The mean and 90% credible interval is shown.| Region | New cases by infection date | Expected change in daily cases | Effective reproduction no. | Doubling time (days) |
|---|---|---|---|---|
| Abruzzo | 73 (31 – 105) | Unsure | 0.9 (0.6 – 1.2) | -18 (17 – Inf) |
| Calabria | 31 (9 – 51) | Likely decreasing | 0.8 (0.5 – 1.1) | -10 (14 – Inf) |
| Campania | 215 (103 – 312) | Likely increasing | 1.3 (0.9 – 1.7) | 12 (5.1 – Inf) |
| Emilia-Romagna | 680 (361 – 936) | Unsure | 0.9 (0.7 – 1.2) | -37 (20 – Inf) |
| Friuli Venezia Giulia | 106 (38 – 166) | Unsure | 1.1 (0.7 – 1.5) | 120 (7.8 – Inf) |
| Lazio | 221 (108 – 320) | Unsure | 1.1 (0.8 – 1.4) | 60 (8.7 – Inf) |
| Liguria | 228 (107 – 332) | Unsure | 1.1 (0.7 – 1.5) | 57 (8 – Inf) |
| Lombardia | 1538 (905 – 2227) | Likely decreasing | 0.8 (0.6 – 1) | -19 (45 – Inf) |
| Marche | 168 (81 – 240) | Unsure | 1 (0.7 – 1.3) | 1300 (11 – Inf) |
| Piemonte | 660 (343 – 956) | Unsure | 1.1 (0.8 – 1.4) | 45 (8.8 – Inf) |
| Puglia | 160 (65 – 239) | Unsure | 1.2 (0.8 – 1.6) | 21 (6.2 – Inf) |
| Sardegna | 51 (19 – 85) | Unsure | 1.1 (0.7 – 1.4) | 140 (6.9 – Inf) |
| Sicilia | 103 (50 – 156) | Unsure | 0.9 (0.6 – 1.2) | -25 (17 – Inf) |
| Toscana | 365 (166 – 533) | Likely increasing | 1.2 (0.8 – 1.5) | 23 (6.9 – Inf) |
| Trentino-Alto Adige | 210 (94 – 306) | Unsure | 1.1 (0.7 – 1.4) | 39 (7.6 – Inf) |
| Umbria | 37 (14 – 58) | Decreasing | 0.7 (0.4 – 1) | -7.7 (Inf – Inf) |
| Valle d’Aosta | 35 (7 – 59) | Unsure | 1.1 (0.7 – 1.5) | 35 (4.5 – Inf) |
| Veneto | 534 (275 – 749) | Unsure | 1.1 (0.8 – 1.4) | 92 (10 – Inf) |
Abbott, Sam, Joel Hellewell, James D. Munday, and Sebastian Funk. 2020. “NCoVUtils: Utility Functions for the 2019-Ncov Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3635417.
Dipartimento della Protezione Civile. n.d. “Dati Covid-19 Italia.” https://github.com/pcm-dpc/COVID-19.
Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.